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Build Annolid dataset indexes/specs and generate YOLO-ready datasets from labeled data sources.
Installer avec Codex ou Claude Copiez ce prompt, collez-le dans Codex, Claude ou un autre assistant, puis laissez-le vérifier la page du skill et l'installer pour vous.
Basé sur la classification professionnelle SOC
Label video behavior segments from frame-grid images with a defined behavior list, model-ready JSON, resumable outputs, and no_behavior handling for sparse labels.
Use Annolid GUI tools for robust automation across web, PDF, video, and chat controls.
Infer the assay or paradigm from video context, task text, tracked entities, and experimental cues such as social interaction, open field, courtship, resident-intruder, and novel object recognition.
Choose assay-specific features and measurable objectives for behavior analysis, including distances, zones, speed, contact, orientation, and object interaction.
Segment behavior timelines from tracks, pose, contact, speed, and proximity signals into typed intervals with stable labels and rationales.
Keep behavior analysis aligned with assay objectives, controls, reproducibility, and measurable outputs instead of ad hoc summaries.
| name | dataset-create |
| description | Build Annolid dataset indexes/specs and generate YOLO-ready datasets from labeled data sources. |
| metadata | {"annolid":{"requires":{"bins":[]}}} |
Use this skill when the user asks to create or prepare a training dataset.
python -m annolid.engine.cli collect-labels \
--source /path/to/source_a \
--source /path/to/source_b \
--dataset-root /path/to/dataset_root \
--index-file logs/label_index/annolid_dataset.jsonl \
--recursive \
--write-spec \
--spec-path /path/to/dataset_root/labelme_spec.yaml \
--val-size 0.1 \
--test-size 0.1 \
--infer-flip-idx
python -m annolid.engine.cli index-to-yolo \
--index-file /path/to/dataset_root/logs/label_index/annolid_dataset.jsonl \
--output-dir /path/to/output \
--dataset-name YOLO_dataset \
--val-size 0.1 \
--test-size 0.1 \
--task pose
python -m annolid.engine.cli import-deeplabcut-training-data \
--source-dir /path/to/deeplabcut_project \
--labeled-data labeled-data \
--instance-label mouse \
--write-pose-schema
Before finishing, verify:
annolid_dataset.jsonl).images/, labels/, and data.yaml exist.kpt_shape (pose datasets) and split entries.In the final response, always include output file paths.